Inspection Planning Under Execution Uncertainty

Autonomous inspection tasks require path-planning algorithms to efficiently gather observations from points of interest (POIs). However, localization errors in urban environments introduce execution uncertainty, posing challenges to successfully completing such tasks. The existing inspection-planning algorithms do not explicitly address this uncertainty, which can hinder their performance. To overcome this, in this article, we introduce incremental random inspection-roadmap search (IRIS)-under uncertainty (IRIS-U$^{2}$), an inspection-planning algorithm that provides statistical assurances regarding coverage, path length, and collision probability. Our approach builds upon IRIS—our framework for deterministic, highly efficient, and provably asymptotically optimal framework. This extension adapts IRIS to uncertain settings using a refined search procedure that estimates POI coverage probabilities through Monte Carlo (MC) sampling. We demonstrate IRIS-U$^{2}$ through a case study on bridge inspections, achieving improved expected coverage, reduced collision probability, and increasingly precise statistical guarantees as MC samples grow. In addition, we explore bounded suboptimal solutions to reduce computation time while preserving statistical assurances.